Here is a quiz for you. Is predicting crime before it happens: (a) something out of Philip K. Dick’s Minority Report; (b) the subject of of a Department of Homeland Security research project that has recently entered testing; (c) a terrible and dangerous idea which will inevitably be counter-productive and which will levy a high price in terms of civil liberties while providing little to no marginal security; or (d) all of the above.

If you picked (d) you are a winner!

The U.S. Department of Homeland security is working on a project called FAST, the Future Attribute Screening Technology, which is some crazy straight-out-of-sci-fi pre-crime detection and prevention software which may come to an airport security screening checkpoint near you someday soon. Yet again the threat of terrorism is being used to justify the introduction of super-creepy invasions of privacy, and lead us one step closer to a turn-key totalitarian state. This may sound alarmist, but in cases like this a little alarm is warranted. FAST will remotely monitor physiological and behavioral cues, like elevated heart rate, eye movement, body temperature, facial patterns, and body language, and analyze these cues algorithmically for statistical aberrance in an attempt to identify people with nefarious intentions. There are several major flaws with a program like this, any one of which should be enough to condemn attempts of this kind to the dustbin. Lets look at them in turn.

First, predictive software of this kind is undermined by a simple statistical problem known as thefalse-positive paradox. Any system designed to spot terrorists before they commit an act of terrorism is, necessarily, looking for a needle in a haystack. As the adage would suggest, it turns out that this is an incredibly difficult thing to do. Here is why: let’s assume for a moment that 1 in 1,000,000 people is a terrorist about to commit a crime. Terrorists are actually probably much much more rare, or we would have a whole lot more acts of terrorism, given the daily throughput of the global transportation system. Now lets imagine the FAST algorithm correctly classifies 99.99 percent of observations — an incredibly high rate of accuracy for any big data-based predictive model. Even with this unbelievable level of accuracy, the system would still falsely accuse 99 people of being terrorists for every one terrorist it finds. Given that none of these people would have actually committed a terrorist act yet distinguishing the innocent false positives from the guilty might be a non-trivial, and invasive task.